Intelligent telescopic induction type supporting construction device and method for concrete floor post-cast strip

Abstract

The invention relates to the technical field of constructional engineering, in particular to an intelligent telescopic induction type supporting construction device and method for a concrete floor post-cast strip. The method comprises the following steps: performing time-varying characteristic analysis on concrete basic parameters to obtain time-varying characteristic data; calculating temperature-shrinkage coupling effect parameters according to the time-varying characteristic data to obtain temperature-shrinkage coupling parameters; constructing a structural deformation mechanical response tensor according to the time-varying characteristic data; recognizing a deformation sensitive area according to the structural deformation mechanical response tensor; collecting sensor network data, and analyzing a physical quantity mapping relation according to the temperature-shrinkage coupling parameters; constructing a concrete material physical characteristic spectrum according to the physical quantity mapping relation; and recognizing a deformation sensitive area according to the structural deformation mechanical response tensor. According to the method, through accurate prediction, real-time monitoring, active control and support optimization of deformation in the construction process of the concrete floor post-cast strip, the construction safety, efficiency and economical efficiency are improved.

Description

Technical Field

[0001] The invention relates to the technical field of building engineering, and in particular to an intelligent telescopic induction support construction device and method for a post-cast strip of a concrete floor slab. Background Art

[0002] The existing support system design is mainly based on experience and static calculations, which makes it difficult to accurately predict the deformation of the cantilever structures on both sides of the post-casting strip during the construction process. In particular, under the influence of factors such as concrete shrinkage creep and temperature changes, the actual deformation often exceeds expectations. At the same time, the existing monitoring methods mainly rely on manual measurement, which is inefficient and has poor accuracy. It is impossible to grasp the deformation state of the structure in real time, which poses a safety hazard. Even if some instruments such as dial indicators and displacement meters are used, it is difficult to achieve comprehensive and continuous dynamic monitoring, and the data collection and analysis process is cumbersome, making it difficult to effectively guide construction.

[0003] Once the existing support system is installed, its support force is fixed and cannot be adjusted according to the actual deformation of the structure. When the structural deformation is too large, the support force is insufficient, causing the structure to crack or collapse; when the structural deformation is small, the support force will be too large, resulting in material waste. The lack of a mechanism for feedback and adjustment based on real-time deformation data makes it difficult for the support system to adapt to complex construction environments and the time-varying characteristics of concrete.

[0004] In order to ensure construction safety, the existing support system usually needs to occupy a large construction space and has a long support period. This will not only affect the transportation of the construction site and reduce the efficiency of material transportation, but also occupy a large amount of building materials and increase construction costs. This problem is particularly prominent in urban construction environments with limited space, which seriously restricts construction progress and efficiency. The existing support system design lacks optimization, resulting in an excessive number of support points, which further exacerbates the problem of space occupation.

[0005] In summary, the existing support technology for post-casting strip construction has shortcomings in deformation monitoring, support force adjustment and space occupancy. It is urgent to develop more intelligent and efficient support technology to improve construction quality, reduce costs and ensure safety. Summary of the invention

[0006] Based on this, it is necessary to provide a construction device and method for intelligent telescopic induction support of post-cast strips of concrete slabs to solve at least one of the above-mentioned technical problems.

[0007] To achieve the above object, a construction method for intelligent telescopic induction support of post-cast belt of concrete floor slab comprises the following steps:

[0008] Step S1: analyzing the time-varying characteristics of concrete foundation parameters to obtain time-varying characteristic data; calculating the temperature-contraction coupling effect parameters according to the time-varying characteristic data to obtain the temperature-contraction coupling parameters; and constructing the structural deformation mechanical response tensor according to the time-varying characteristic data;

[0009] Step S2: Identify deformation sensitive areas according to the structural deformation mechanical response tensor; collect sensor network data, and analyze the physical quantity mapping relationship according to the temperature-contraction coupling parameter; construct the physical property spectrum of concrete material according to the physical quantity mapping relationship;

[0010] Step S3: acquiring real-time monitoring data; performing structural force analysis based on the real-time monitoring data to obtain internal force distribution data; determining a support point arrangement scheme based on the internal force distribution data and the deformation sensitive area; performing a physical parameter-support force response analysis based on the physical property spectrum of the concrete material and the support point arrangement scheme to obtain a physical parameter-support force response function; and using the physical parameter-support force response function to regulate the support force in real time;

[0011] Step S4: Acquire measured deformation data; perform deformation characteristic anisotropy analysis based on the measured deformation data to obtain deformation anisotropy characteristics; perform structural mechanics transfer path analysis based on the deformation anisotropy characteristics to obtain a mechanics transfer path diagram; perform support layout optimization and control efficiency evaluation on the support point arrangement scheme based on the mechanics transfer path diagram to obtain a structural deformation control efficiency index.

[0012] The present invention establishes an accurate constitutive model of concrete materials by analyzing the time-varying characteristics of concrete foundation parameters and considering the temperature-contraction coupling effect. It can accurately predict the deformation behavior of concrete at different ages and in different temperature and humidity environments. At the same time, the constructed structural deformation mechanical response tensor (SDMT) comprehensively describes the deformation characteristics of the structure under load and environmental effects, providing a reliable theoretical basis for subsequent structural analysis, monitoring and control, effectively improving the accuracy and reliability of deformation prediction, and reducing the safety risks caused by inaccurate deformation prediction. By identifying deformation-sensitive areas and deploying multi-parameter sensor arrays, comprehensive, real-time and accurate monitoring of the deformation state of the post-casting zone area is achieved; the established strain-temperature-humidity mapping relationship effectively eliminates the interference of environmental factors on strain measurement and improves measurement accuracy; the constructed concrete material physical property spectrum (CMPS) deeply reveals the deformation mechanism of concrete materials, provides key material performance data for supporting force regulation, enables the monitoring system to timely and accurately reflect the true deformation state of the structure, and provides a guarantee for timely discovery of potential risks. Through real-time monitoring data and structural force analysis, the internal force distribution and deformation state of the post-casting zone can be accurately grasped, providing a scientific basis for the arrangement of support points; the constructed physical parameter-support force response function (PSRF) establishes the mapping relationship between the physical properties of concrete and the optimal support force, realizing the precise control of the support force based on material properties; the variable stiffness hydraulic support system can automatically adjust the support stiffness and support force according to the real-time deformation state of the structure, realizing the active control of the structural deformation, effectively avoiding the structural cracking or instability caused by improper support force, and improving the safety and reliability of construction. Through anisotropic analysis and structural mechanics transfer path analysis of the measured deformation data, the inherent law of structural deformation and mechanical transmission mechanism are deeply revealed, providing scientific guidance for the optimization of support layout; based on the mechanical contribution matrix and greedy algorithm, a "few but fine" support strategy is realized, the number of support points is reduced, and the construction cost is reduced; the constructed structural deformation control efficiency index (SCEI) comprehensively considers the mechanical contribution, material efficiency and space utilization, realizes a comprehensive evaluation of the support layout plan, ensures the effectiveness and economy of the support system, improves construction efficiency and reduces engineering cost.

[0013] Therefore, the present invention realizes accurate prediction of deformation during the construction of the post-casting strip by establishing an accurate mathematical model between concrete material properties and structural deformation; utilizes a multi-parameter sensor array and data calibration technology to realize comprehensive, real-time and accurate monitoring of structural deformation, overcoming the shortcomings of low efficiency and poor accuracy of traditional manual measurement; designs a variable stiffness hydraulic support system, and realizes intelligent regulation of the support force based on a physical parameter-support force response function (PSRF), which can automatically adjust the support force according to the real-time deformation state of the structure, avoiding the problem of insufficient or excessive support force; through mechanical transfer path analysis and optimization algorithm, the optimization of support layout is realized, the number of support points is reduced, and the space utilization rate and material efficiency are improved, thereby solving the problems of inaccurate prediction, delayed monitoring, fixed support force and large space occupancy of the existing support system.

[0014] Preferably, the present invention further provides a concrete floor post-casting belt intelligent telescopic inductive support construction device, comprising a support device main body, a sensor network part, a data processing control part and a power supply part, wherein the sensor network part is installed on the support device main body, the power supply part is installed inside the support device main body, and the data processing control part is electrically connected to the power supply part, and is used to execute the above-mentioned concrete floor post-casting belt intelligent telescopic inductive support construction method, wherein the data processing control part of the concrete floor post-casting belt intelligent telescopic inductive support construction device comprises:

[0015] The structural deformation characteristic modeling module is used to analyze the time-varying characteristics of concrete foundation parameters to obtain time-varying characteristic data; calculate the temperature-contraction coupling effect parameters based on the time-varying characteristic data to obtain the temperature-contraction coupling parameters; and construct the structural deformation mechanical response tensor based on the time-varying characteristic data;

[0016] Intelligent monitoring module, used to identify deformation sensitive areas according to the mechanical response tensor of structural deformation; collect sensor network data and analyze physical quantity mapping relationship according to temperature-contraction coupling parameters; construct physical property spectrum of concrete material according to physical quantity mapping relationship;

[0017] The support force intelligent control module is used to obtain real-time monitoring data; perform structural force analysis based on the real-time monitoring data to obtain internal force distribution data; determine the support point layout plan based on the internal force distribution data and deformation sensitive areas; perform physical parameter-support force response analysis based on the physical property spectrum of concrete materials and the support point layout plan to obtain the physical parameter-support force response function; and use the physical parameter-support force response function to control the support force in real time;

[0018] The support layout optimization module is used to obtain the measured deformation data; perform anisotropy analysis of deformation characteristics based on the measured deformation data to obtain the anisotropic characteristics of deformation; perform structural mechanics transfer path analysis based on the anisotropic characteristics of deformation to obtain a mechanics transfer path diagram; perform support layout optimization and control efficiency evaluation on the support point arrangement scheme based on the mechanics transfer path diagram to obtain the structural deformation control efficiency index. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A schematic diagram of the steps of a construction method for intelligent telescopic induction support of a post-cast belt of a concrete floor;

[0020] Figure 2 It is a schematic diagram of the detailed implementation steps of step S1 in the present invention.

[0021] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings in conjunction with the embodiments. DETAILED DESCRIPTION

[0022] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0023] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0024] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0025] In the embodiment of the present invention, reference Figure 1The figure is a schematic diagram of the steps of the intelligent telescopic induction support construction method for the post-casting belt of the concrete floor according to the present invention. In this example, the intelligent telescopic induction support construction method for the post-casting belt of the concrete floor comprises the following steps:

[0026] Step S1: analyzing the time-varying characteristics of concrete foundation parameters to obtain time-varying characteristic data; calculating the temperature-contraction coupling effect parameters according to the time-varying characteristic data to obtain the temperature-contraction coupling parameters; and constructing the structural deformation mechanical response tensor according to the time-varying characteristic data;

[0027] In the embodiment of the present invention, the compressive strength, elastic modulus and free shrinkage strain of concrete at different ages are measured by standard tests, and the change laws of elastic modulus, shrinkage strain and creep coefficient with time are predicted by using B3 model, CEB-FIP model and double power law formula respectively, and the temperature and humidity coupling effect analysis is carried out to obtain the time-varying curve of temperature-shrinkage coupling effect parameter (TSCP); finally, a three-dimensional finite element model of the floor slab is established by using ANSYS software, the time-varying material parameters of concrete are defined, the load is applied for static analysis, the unit strain is extracted, and the structural deformation mechanical response tensor (SDMT) containing elastic, plastic and time-varying strain components is constructed.

[0028] Step S2: Identify deformation sensitive areas according to the structural deformation mechanical response tensor; collect sensor network data, and analyze the physical quantity mapping relationship according to the temperature-contraction coupling parameter; construct the physical property spectrum of concrete material according to the physical quantity mapping relationship;

[0029] In the embodiment of the present invention, the strain gradient is calculated according to the SDMT to identify the deformation sensitive area, and the sensing nodes integrating the fiber Bragg grating strain, temperature and resistive humidity sensors are densely arranged in the area, and the data is collected in real time by the fiber demodulator; the strain-temperature-humidity mapping relationship is established, and the strain measurement value is corrected; the strain rate, temperature change rate and humidity change rate are calculated, and the multi-dimensional relationship curve is drawn to construct the concrete material physical property spectrum (CMPS) to provide material performance data for subsequent analysis.

[0030] Step S3: acquiring real-time monitoring data; performing structural force analysis based on the real-time monitoring data to obtain internal force distribution data; determining a support point arrangement scheme based on the internal force distribution data and the deformation sensitive area; performing a physical parameter-support force response analysis based on the physical property spectrum of the concrete material and the support point arrangement scheme to obtain a physical parameter-support force response function; and using the physical parameter-support force response function to regulate the support force in real time;

[0031] In an embodiment of the present invention, the measured deformation data is compared and calibrated with the SDMT prediction value, the deformation gradient and the stress field are calculated, the internal force of the key section is obtained by stress integration, the internal force imbalance and the critical area are analyzed, and the required support force is calculated; a variable stiffness hydraulic support system is designed; a model test is carried out, parameters such as temperature, humidity and age are controlled, support force-strain data are collected, single parameter response, parameter interaction and time-varying response characteristics are analyzed, and a physical parameter-support force response function (PSRF) is constructed; based on the PSRF and real-time monitoring data, a closed-loop control algorithm is used to dynamically adjust the support force and stiffness.

[0032] Step S4: obtaining measured deformation data; performing deformation characteristic anisotropy analysis according to the measured deformation data to obtain deformation anisotropy characteristics; performing structural mechanical transfer path analysis according to the deformation anisotropy characteristics to obtain a mechanical transfer path diagram; performing support layout optimization and control efficiency evaluation on the support point arrangement scheme according to the mechanical transfer path diagram to obtain a structural deformation control efficiency index;

[0033] In the embodiment of this aspect, the measured strain is converted into displacement, the directional strain field is calculated, the deformation characteristic tensor is constructed in combination with CMPS, the principal axis analysis and anisotropy classification are performed, and the main control direction of deformation is determined; the force flow vector field is calculated by the internal force gradient, the main flow path is extracted, the key nodes are identified, the node coupling relationship is analyzed, the main force transmission link is constructed, and the support control area is divided; the mechanical contribution of the support points is evaluated one by one to form a contribution matrix; the greedy algorithm is used to optimize the support layout; the structural deformation control effectiveness index (SCEI) is constructed, the mechanical contribution, material efficiency and space utilization of the support layout are comprehensively evaluated, and the optimal solution is selected.

[0034] As an example of the present invention, refer to Figure 2 As shown, in this example, step S1 includes:

[0035] Step S11: collecting concrete foundation parameters and performing characteristic measurement to obtain concrete foundation characteristic data;

[0036] In the embodiment of the present invention, a 150mm×150mm×150mm cube test block and a 150mm×150mm×300mm prism test block are prepared in accordance with the national standard "Standard for Test Methods of Mechanical Properties of Ordinary Concrete" (GB / T50081-2019), and the test blocks are cured under the same conditions as the concrete floor slabs at the construction site. After curing for 7 days, 14 days, and 28 days, the compressive strength of the cube test blocks is tested using a universal testing machine, and the peak loads Fcu,7, Fcu,14, and Fcu,28 of the three test blocks are recorded, and the compressive strengths fcu,7=Fcu,7 / 22500, fcu,14=Fcu,14 / 22500, and fcu,28=Fcu,28 / 22500 are calculated, in MPa. The elastic modulus tester is used to test the elastic modulus of the prism test block, and resistance strain gauges are pasted at the center of both sides of the test block, and static resistance strain gauges are connected. Load uniformly at a rate of 0.5 MPa / s, record the stresses σ1 and σ2 when the stress reaches 40% of the compressive strength, and the corresponding strains ε1 and ε2, calculate the elastic modulus Ec,7=(σ2-σ1) / (ε2-ε1), repeat the test on three test blocks, and obtain Ec,14 and Ec,28. The free shrinkage strain of the prism specimens under standard curing conditions (temperature 20±2°C, relative humidity above 95%) was measured using an electronic comparator, and the length changes L3, L7, L14, L28, L56, and L90 were recorded at 3, 7, 14, 28, 56, and 90 days, and the free shrinkage strain εs was calculated. 3 = (L0-L3) / L0, εs, 7 = (L0-L7) / L0, εs, 14 = (L0-L14) / L0, εs, 28 = (L0-L28) / L0, εs, 56 = (L0-L56) / L0, εs, 90 = (L0-L90) / L0, where L0 is the initial length. The Poisson's ratio μ of concrete was measured using a Poisson's ratio tester. All the above test data, including fcu,7, fcu,14, fcu,28, Ec,7, Ec,14, Ec,28, εs,3, εs,7, εs,14, εs,28, εs,56, εs,90, and μ, are organized into a tabular form as a database of concrete foundation characteristics.

[0037] Step S12: performing time-varying characteristic evolution processing according to the concrete foundation characteristic data to obtain time-varying characteristic data;

[0038] In the embodiment of the present invention, based on the concrete foundation characteristic database obtained in step S11, the B3 model is used to predict the change of concrete elastic modulus over time. In the B3 model, the calculation formula of the elastic modulus E(t) is: E(t) = E28×[1+q2×(t-t0)^(-0.1)+q3×(t-t0)^(-0.2)+q4×(t-t0)^(-0.5)], where E28 is the 28-day elastic modulus, t is time (days), t0 is the loading age, q2, q3, and q4 are empirical coefficients, which are determined by fitting the measured data using the least squares method. According to the measured εs, 3, εs, 7, εs, 14, εs, 28, εs, 56, and εs, 90, the CEB-FIP model is used to predict the change of concrete shrinkage strain over time. In the CEB-FIP model, the calculation formula of shrinkage strain εcs(t, ts) is: εcs(t, ts) = εcso × βs(t-ts), where εcso is the nominal shrinkage coefficient, βs(t-ts) is the shrinkage development coefficient, and ts is the shrinkage start time. The calculation formula of εcso is: εcso = εs(fcm) × βRH, where εs(fcm) is the coefficient related to the compressive strength of concrete, and βRH is the coefficient related to the relative humidity. The calculation formula of βs(t-ts) is: βs(t-ts) = [(t-ts) / (350×(h / h0)^2+(t-ts))]^0.5, where h is the theoretical thickness of the component, and h0 = 100mm. According to the measured data, the creep coefficient φ(t, t0) is used to describe the creep characteristics of concrete. The creep coefficient is defined as the ratio of the total strain at time t to the instantaneous elastic strain when the load is applied at time t0 under continuous load. The double power law formula is used to describe the creep coefficient: φ(t, t0) = φ0 × [(t-t0)^m], where φ0 is the nominal creep coefficient and m is the empirical coefficient determined by fitting test data. The calculated data of E(t), εcs(t, ts) and φ(t, t0) changing with time are stored in a table to form time-varying characteristic data.

[0039] Step S13: Calculating the temperature-contraction coupling effect parameters according to the time-varying characteristic data to obtain the temperature-contraction coupling parameters;

[0040] In the embodiment of the present invention, standard-sized concrete prism specimens are prepared, and the free shrinkage strains at different ages are measured under standard curing conditions, and the shrinkage sensitivity coefficient is obtained by fitting; shrinkage tests are carried out under different constant temperature and humidity conditions, and the temperature correction coefficient (KT) and the humidity correction coefficient (KH) are calculated, and the temperature correction function and the humidity correction function are fitted respectively; an orthogonal experiment is designed, and the shrinkage deformation of concrete is measured under different temperature and humidity combination conditions, and the temperature-humidity interaction coefficient (KTH) is calculated to form an interaction matrix; based on KT, KH and KTH, a temperature-shrinkage coupling deformation prediction model is constructed, and the accuracy of the model is verified by measured data; the actual temperature and humidity parameters of the engineering environment are substituted into the model, the temperature-shrinkage coupling effect parameter (TSCP) is calculated, and its evolution law with time is analyzed, and finally the time-varying curve of TSCP is obtained.

[0041] Step S14: acquiring engineering structure data, and constructing a structural deformation mechanical response tensor according to the time-varying characteristic data;

[0042] In an embodiment of the present invention, the structural design drawings of the floor where the post-cast strip is located are obtained, including geometric dimensions, reinforcement information, load conditions, etc. A three-dimensional finite element model of the floor is established using the general finite element software ANSYS. The SOLID185 unit is used to simulate concrete. The unit is an 8-node hexahedral unit with three translational degrees of freedom. The LINK180 unit is used to simulate the steel bar. The unit is a 2-node rod unit with only axial tension and compression stiffness. According to the time-varying characteristic data of concrete, the elastic modulus E(t) and the shrinkage strain εcs(t, ts) of concrete are defined in ANSYS as a function of time. According to the design drawings, a dead load and a live load are applied to the model. For the cantilevered parts on both sides of the post-cast strip, temporary loads during construction are considered. Full constraints are used to simulate the connection between the two sides of the post-cast strip and the cast parts. Static analysis is performed to solve the displacement field and stress field of the structure at different times. The strain results of each unit are extracted, including three positive strain components εx, εy, εz and three shear strain components γxy, γyz, γxz. The strain is decomposed into three parts: elastic strain εe, plastic strain εp and time-varying strain εt (including shrinkage strain and creep strain). The structural deformation mechanical response tensor SDMT is constructed. SDMT is a third-order tensor, and its components are expressed as: SDMT = [εe, εp, εt]. SDMT contains the deformation information of the structure at different times and positions, reflecting the overall mechanical response of the structure. The SDMT data is output in matrix form and stored as structural deformation mechanical response tensor data.

[0043] Preferably, step S13 comprises the following steps:

[0044] Step S131: determining the shrinkage sensitivity coefficient of concrete;

[0045] Step S132: constructing a temperature influence correction curve according to the shrinkage sensitivity coefficient to obtain a temperature correction function;

[0046] Step S133: constructing a humidity influence correction curve according to the shrinkage sensitivity coefficient to obtain a humidity correction function;

[0047] Step S134: performing temperature-humidity interaction analysis according to the temperature correction function and the humidity correction function to obtain an interaction influence matrix;

[0048] Step S135: constructing a temperature-contraction coupling model according to the interaction influence matrix, the temperature correction function and the humidity correction function to obtain a coupled deformation prediction model;

[0049] Step S136: Calculating temperature-contraction coupling effect parameters using the coupled deformation prediction model;

[0050] Step S137: performing coupling effect time-varying characteristic analysis according to the temperature-contraction coupling effect parameters and the time-varying characteristic data to obtain the temperature-contraction coupling parameters.

[0051] In the embodiment of the present invention, a 100mm×100mm×515mm prism specimen is prepared in accordance with the national standard "Standard for Test Methods for Long-term Performance and Durability of Ordinary Concrete" (GB / T50082-2009), and the specimen is cured under the same conditions as the concrete floor slab at the construction site. Stainless steel probes are embedded at both ends of the specimen, and the probe spacing is 400mm. The specimen is placed in a standard curing box (temperature 20±2°C, relative humidity 60±5%). The length of the specimen is measured using an electronic comparator 3 days, 7 days, 14 days, 28 days, 56 days, and 90 days after the concrete is poured, and the readings are recorded as L0,3, L0,7, L0,14, L0,28, L0,56, and L0,90. The shrinkage strains at different ages under standard curing conditions were calculated as follows: ε0,3 = (L0-L0,3) / 400, ε0,7 = (L0-L0,7) / 400, ε0,14 = (L0-L0,14) / 400, ε0,28 = (L0-L0,28) / 400, ε0,56 = (L0-L0,56) / 400, ε0,90 = (L0-L0,90) / 400, where L0 is the initial length. The measured data were fitted using the formula ε0 = a×(1-e^(-b×t)) to obtain the fitting coefficients a and b, where t is the age (days). The shrinkage sensitivity coefficient S1 = Δε / ε0 was calculated, where Δε is the shrinkage strain change caused by the unit environmental parameter change, and ε0 is the shrinkage strain under standard conditions. For example, when calculating the temperature sensitivity coefficient, Δε is the shrinkage strain change caused by a temperature change of 1°C; when calculating the humidity sensitivity coefficient, Δε is the shrinkage strain change caused by a relative humidity change of 1%. The shrinkage sensitivity coefficients S1 of different ages (3 days, 7 days, 14 days, 28 days, 56 days, and 90 days) are sorted into a table to form a shrinkage sensitivity coefficient table.

[0052] Prepare prism specimens with the same size and curing conditions as step S131. Place the specimens in a constant temperature curing box at 5°C, 15°C, 25°C, and 35°C, respectively, and control the relative humidity to 60±5%. Use an electronic comparator to measure the length of the specimens 3 days, 7 days, 14 days, 28 days, 56 days, and 90 days after concrete pouring, and record the readings LT, 3, LT, 7, LT, 14, LT, 28, LT, 56, and LT, 90, where T represents temperature. Calculate the shrinkage strains under different temperature conditions: εT,3 = (L0-LT, 3) / 400, εT,7 = (L0-LT, 7) / 400, εT,14 = (L0-LT, 14) / 400, εT,28 = (L0-LT, 28) / 400, εT,56 = (L0-LT, 56) / 400, εT,90 = (L0-LT, 90) / 400. Calculate the temperature correction coefficient KT = εT / ε0, where εT is the shrinkage strain at temperature T, and ε0 is the shrinkage strain under standard conditions (20°C). Use the quadratic polynomial KT = m1×(T-20)2+m2×(T-20)+1 to fit the temperature correction coefficient, and get the temperature influence coefficients m1 and m2. Establish a temperature-shrinkage relationship curve, with the horizontal axis being the temperature T and the vertical axis being the temperature correction coefficient KT. This curve describes the influence of temperature on the shrinkage and deformation of concrete. The temperature correction coefficient at any temperature can be obtained through this curve.

[0053] Prepare prismatic specimens with the same size and curing conditions as step S131. Place the specimens in a constant humidity curing box with a relative humidity of 40%, 50%, 70%, and 80%, respectively, and control the temperature to 20±2°C. Use an electronic comparator to measure the length of the specimens 3 days, 7 days, 14 days, 28 days, 56 days, and 90 days after concrete pouring, and record the readings LH, 3, LH, 7, LH, 14, LH, 28, LH, 56, and LH, 90, where H represents relative humidity. Calculate the shrinkage strain under different humidity conditions: εH,3 = (L0-LH, 3) / 400, εH,7 = (L0-LH, 7) / 400, εH,14 = (L0-LH, 14) / 400, εH,28 = (L0-LH, 28) / 400, εH,56 = (L0-LH, 56) / 400, εH,90 = (L0-LH, 90) / 400. Calculate the humidity correction coefficient KH = εH / ε0, where εH is the shrinkage strain under relative humidity H, and ε0 is the shrinkage strain under standard conditions (60%). Use the exponential function KH = n1×e^(n2×(60-H)) to fit the humidity correction coefficient and obtain the humidity influence coefficients n1 and n2. Establish a humidity-shrinkage relationship curve, with the horizontal axis being the relative humidity H and the vertical axis being the humidity correction coefficient KH. This curve describes the influence of humidity on the shrinkage and deformation of concrete. The humidity correction coefficient under any humidity can be obtained through this curve.

[0054] Design an orthogonal test to measure the shrinkage deformation of concrete under different temperature and humidity combinations. Select four temperature levels: 5°C, 15°C, 25°C, and 35°C, and four humidity levels: 40%, 50%, 70%, and 80%. A total of 16 groups of temperature and humidity combinations are formed. Prepare prismatic specimens with the same size and curing conditions as step S131. Place the specimens in constant temperature and humidity chambers corresponding to the temperature and humidity combinations. Use an electronic comparator to measure the length of the specimens 3 days, 7 days, 14 days, 28 days, 56 days, and 90 days after concrete pouring, and record the readings LTH, 3, LTH, 7, LTH, 14, LTH, 28, LTH, 56, and LTH, 90, where T represents temperature and H represents relative humidity. Calculate the shrinkage strain under different temperature and humidity combinations: εTH,3 = (L0-LTH, 3) / 400, εTH,7 = (L0-LTH, 7) / 400, εTH,14 = (L0-LTH, 14) / 400, εTH,28 = (L0-LTH, 28) / 400, εTH,56 = (L0-LTH, 56) / 400, εTH,90 = (L0-LTH, 90) / 400. Calculate the temperature-humidity interaction effect ΔεTH = εTH-(KT×KH×ε0), where εTH is the measured shrinkage value, KT and KH are the temperature and humidity correction coefficients obtained by steps S132 and S133, respectively, and ε0 is the shrinkage strain under standard conditions. Establish the interaction effect coefficient KTH = ΔεTH / (KT×KH×ε0). Arrange the KTH values ​​under 16 groups of temperature and humidity combinations into a 4×4 matrix to form a temperature-humidity interaction effect matrix. This matrix describes the nonlinear effect of temperature and humidity on concrete shrinkage deformation.

[0055] Based on the temperature correction function, humidity correction function and interaction matrix obtained in steps S132, S133 and S134, a temperature-shrinkage coupling deformation prediction model is constructed. The calculation formula for the modified shrinkage strain is established: ε=ε0×[KT×KH+γ×KTH], where ε is the predicted shrinkage strain, ε0 is the shrinkage strain under standard conditions, KT is the temperature correction coefficient, KH is the humidity correction coefficient, KTH is the temperature-humidity interaction coefficient, and γ is the interaction weight coefficient. The least squares method is used, and the measured shrinkage strain data is taken as the target value to fit the interaction weight coefficient γ so that the sum of squares of the error between the model prediction value and the measured value is minimized. A three-dimensional surface equation is established, with temperature T and relative humidity H as independent variables and the predicted shrinkage strain ε as the dependent variable. The surface equation describes the predicted value of concrete shrinkage deformation under different temperature and humidity combinations. The established model is verified. Temperature and humidity conditions different from the modeling test are selected to carry out concrete shrinkage deformation tests, and the test results are compared with the model prediction results. The relative error of the model prediction is calculated as RE = |(ε_predicted-ε_measured) / ε_measured|×100%, where ε_predicted is the shrinkage strain predicted by the model and ε_measured is the measured shrinkage strain. Ensure that the relative error of all verification points is less than 15%. The final coupled deformation prediction model is ε = ε0×[K_T×K_H+γ×K_TH], where the calculation method of ε0, K_T, K_H, K_TH and γ is as described above. This model takes into account the effects of temperature, humidity and their interactions on concrete shrinkage deformation.

[0056] Substitute the actual environmental parameters of the project (including the data on the change of temperature and humidity over time) into the coupled deformation prediction model obtained in step S135. These environmental parameters are collected in real time by the temperature and humidity sensors installed on site. For example, if the ambient temperature at a certain moment is 25°C and the relative humidity is 70%, then substitute T=25 and H=70 into the model to calculate the predicted shrinkage strain ε at that moment. Calculate the temperature-shrinkage coupling effect parameter TSCP according to the formula TSCP=(ε-ε0) / ε0. Where ε is the shrinkage strain predicted by the model, and ε0 is the shrinkage strain under standard conditions (obtained by step S131). The TSCP value reflects the degree of deviation of the shrinkage deformation of concrete under actual environmental conditions from the standard conditions. If TSCP>0, it means that the actual environment accelerates the shrinkage of concrete; if TSCP<0, it means that the actual environment inhibits the shrinkage of concrete. For different areas of the project (for example, areas directly exposed to sunlight, shadow areas, indoor areas, etc.), calculate the TSCP values ​​separately to form a spatial distribution map of TSCP.

[0057] Combine the TSCP value calculated in step S136 and the concrete time-varying characteristic data obtained in step S12 (especially the variation of shrinkage strain over time), and analyze the variation trend of TSCP over time. At different ages (for example, 3 days, 7 days, 14 days, 28 days, 56 days, 90 days), calculate the TSCP value and draw the TSCP-t curve, where t is the age. Use the function form TSCP(t)=p1×(1-e^(-p2×t)) to fit the TSCP-t curve to obtain the time-varying coefficients p1 and p2. This function describes the variation of TSCP over time. Combine the creep characteristics of concrete, predict the long-term (for example, 1 year, 2 years or even longer) TSCP variation trend. Consider the amplification effect of creep on shrinkage deformation, and correct the long-term prediction value of TSCP. Finally, the TSCP time-varying curve containing short-term and long-term predictions is obtained, which is the final temperature-shrinkage coupling parameter. This parameter comprehensively considers the effects of temperature, humidity, temperature-humidity interaction and time factors on concrete shrinkage and deformation, providing a key basis for the design and regulation of the supporting force of the post-cast strip.

[0058] Preferably, step S2 comprises the following steps:

[0059] Step S21: identifying deformation sensitive areas according to the structural deformation mechanical response tensor;

[0060] Step S22: deploying a multi-parameter sensor array on the deformation sensitive area;

[0061] Step S23: collecting sensor network data using a multi-parameter sensor array;

[0062] Step S24: analyzing the strain-temperature-humidity three-dimensional mapping relationship according to the sensor network data and the temperature-contraction coupling parameter to obtain a physical quantity mapping relationship;

[0063] Step S25: generating a strain correction value according to the physical quantity mapping relationship and the sensor network data;

[0064] Step S26: constructing a physical property spectrum of concrete material according to the strain correction value and the sensor network data.

[0065] In the embodiment of the present invention, strain data is extracted from the structural deformation mechanical response tensor (SDMT) obtained in step S14. SDMT is a third-order tensor, and its component form is SDMT = [εe, εp, εt], which represents elastic strain, plastic strain and time-varying strain respectively. Calculate the strain gradient. For each unit, calculate its strain gradient in the three directions of x, y and z: The central difference method is used to calculate the numerical gradient. For example, for element i, the strain gradient in the x direction is calculated as: Where εx,i+1 and εx,i-1 are the x-direction strain components of the adjacent units of unit i in the x-direction, and xi+1 and xi-1 are the x-coordinates of the adjacent units. Calculate the strain gradient modulus: According to the strain gradient modulus The size of the deformation sensitive area is identified. Set the threshold, for example, The area with a value greater than 1.5 times the average value is defined as the deformation sensitive area. On the finite element model, the deformation sensitive areas are marked with different colors to form a deformation sensitive area distribution map.

[0066] According to the distribution map of deformation sensitive areas obtained in step S21, the sensor layout position is determined. In areas with large deformation gradients, the sensor density is appropriately increased; in areas with small deformation gradients, the sensor density is appropriately reduced. Fiber Bragg grating strain sensors are selected to measure concrete strain, model OS3155, with a range of ±2500με and an accuracy of ±1με. Fiber Bragg grating temperature sensors are selected to measure concrete temperature, model OS4100, with a range of -20℃ to 80℃ and an accuracy of ±0.1℃. Resistive humidity sensors are selected to measure the internal humidity of concrete, model HIH-4000, with a range of 0-100%RH and an accuracy of ±2%RH. The strain sensor, temperature sensor and humidity sensor are integrated into one sensing node. Distributed optical fiber sensing technology is used to connect multiple sensing nodes in series into a sensing array. At the bottom of the cantilevered slab on both sides of the post-casting strip, a sensing node is arranged every 1m along the length of the slab, and a sensing node is arranged every 0.5m along the width of the slab. The sensor is pasted on the concrete surface and protected with epoxy resin. Connect the fiber optic demodulator, model si255, to collect and process the sensor signals.

[0067] Start the fiber optic interrogator and set the sampling frequency to 1 Hz, that is, collect data once per second. The fiber optic interrogator sends an optical signal to each sensor node through the optical fiber and receives the reflected optical signal. According to the wavelength change of the optical signal, the strain, temperature and humidity values ​​of each sensor node are calculated. The collected data is transmitted to the data acquisition computer in real time. The data acquisition computer stores the data as a file in CSV format, and the file name contains the acquisition time and sensor number. The data file contains the following fields: timestamp, sensor number, strain value, temperature value, humidity value.

[0068] Extract strain, temperature and humidity data from the sensor network data obtained in step S23. Obtain the temperature-contraction coupling parameter TSCP(t) from step S137. Since the strain value measured by the sensor contains the additional strain caused by temperature and humidity changes, it needs to be corrected. Establish a three-dimensional mapping relationship between strain, temperature and humidity. For each sensor node, establish a strain correction model: εcorrected = εmeasured-εT-εH, where εcorrected is the corrected strain, εmeasured is the strain measured by the sensor, εT is the strain caused by temperature, and εH is the strain caused by humidity. The calculation formula of εT is: εT = α×(T-T0), where α is the thermal expansion coefficient of concrete, T is the temperature measured by the sensor, and T0 is the reference temperature (usually 20°C). The calculation formula of εH is: εH = ε0×[K_T×K_H+γ×K_TH-1], where the definitions and values ​​of ε0, K_T, K_H, K_TH and γ are obtained through the sub-step of S13, and the temperature T and humidity H measured by the sensor are brought in. In this way, a mapping relationship between strain, temperature and humidity is established, and the measured strain data can be corrected to the real structural strain that is not affected by temperature and humidity.

[0069] According to the strain-temperature-humidity three-dimensional mapping relationship obtained in step S24, the original strain data collected in step S23 is corrected. For each strain value εmeasured measured by the sensor, the corrected strain value εcorrected is calculated according to its corresponding temperature value T and humidity value H using the formula εcorrected=εmeasured-εT-εH. The corrected strain values ​​of all sensor nodes are arranged in chronological order to form strain correction value time series data. The strain correction value data is stored in a file in CSV format, and the file name contains the acquisition time and sensor number.

[0070] Integrate the strain correction value time series data obtained in step S25 with the temperature and humidity data obtained in step S23. Calculate the strain rate. For each sensing node, calculate the strain difference between two adjacent time points Δε=εcorrected,t+1-εcorrected,t, where εcorrected,t+1 and εcorrected,t are the corrected strain values ​​at time t+1 and time t, respectively. Calculate the strain rate: ε · = Δε / Δt, where Δt is the time interval (1 second). Calculate the temperature change rate and humidity change rate. Use the same method to calculate the temperature change rate T · and humidity change rate H ·. Establish a time-strain rate relationship curve. For each sensing node, draw a curve with time as the horizontal axis and strain rate as the vertical axis. Establish a temperature-deformation rate relationship curve. For each sensing node, draw a curve with temperature as the horizontal axis and strain rate as the vertical axis. Establish a humidity-shrinkage rate relationship curve. For each sensing node, draw a curve with relative humidity as the horizontal axis and strain rate as the vertical axis. Combine all the above curves together to form the concrete material physical property spectrum (CMPS). CMPS contains multi-dimensional information such as time, strain rate, temperature, humidity, etc., which reflects the deformation characteristics of concrete materials under different environmental conditions. Store CMPS data in the form of a three-dimensional matrix.

[0071] Preferably, the structural stress analysis in step S3 includes:

[0072] Perform monitoring data calibration and integration on the real-time monitoring data and the mechanical response tensor of the structural deformation to obtain a calibrated deformation field;

[0073] generating a deformation gradient field according to the calibration deformation field;

[0074] The stress field is derived from the deformation gradient field to obtain the structural stress field;

[0075] Calculate the cross-sectional internal force of the structural stress field and obtain the key cross-sectional internal force data;

[0076] Conduct mechanical imbalance analysis on the internal force data of key sections to obtain the mechanical imbalance distribution diagram;

[0077] According to the mechanical imbalance distribution map and the calibrated deformation field, the critical area is identified to obtain the critical control area map;

[0078] Calculate the required support force based on the critical control area diagram and the key section internal force data to obtain the support force distribution data;

[0079] Generate internal force distribution data based on support force distribution data and critical control area diagram.

[0080] In an embodiment of the present invention, the strain correction value data of real-time monitoring is obtained from step S25 and converted into displacement data. For each sensing node, its displacement components in the three directions of x, y, and z are calculated according to its position and strain value. For example, for the strain sensor arranged along the x direction, its displacement calculation formula is: ux=εx×L, where εx is the corrected x-direction strain, and L is the effective length of the sensor. The initial predicted displacement field of the structural deformation mechanical response tensor (SDMT) is obtained from step S14. The deviation between the measured displacement and the predicted displacement is calculated. For each sensing node, its displacement deviation in the three directions of x, y, and z is calculated: Δux=ux, measured-ux, predicted, Δuy=uy, measured-uy, predicted, Δuz=uz, measured-uz, predicted, where ux, measured, uy, measured, uz, measured are measured displacement components, and ux, predicted, uy, predicted, uz, predicted are predicted displacement components. The displacement prediction in SDMT is corrected by the weighted average method. For each finite element unit, the corrected displacement value is calculated based on the displacement deviation of its surrounding sensor nodes. For example, for unit i, its corrected displacement in the x direction is calculated as: ux,corrected,i = ux,predicted,i + ∑(wi×Δux,j), where wi is the weight coefficient, which is inversely proportional to the distance from unit i to sensor node j, and ∑ represents the sum of all adjacent sensor nodes. Through the spatial interpolation algorithm, the discrete corrected displacement value is extended to the entire calculation domain to generate a continuous calibration deformation field.

[0081] Based on the calibration deformation field obtained in the previous step, the spatial deformation gradient tensor is calculated. The numerical approximation of the deformation gradient is calculated using the finite difference method. For each finite element, the deformation gradient in the x, y, and z directions is calculated: For example, for unit i, its deformation gradient in the x direction is calculated as: Where ux,i+1 and ux,i-1 are the x-direction displacement components of the adjacent units of unit i in the x-direction, and xi+1 and xi-1 are the x-coordinates of the adjacent units. Similarly, the shear deformation gradient is calculated: The calculated deformation gradients are combined into the deformation gradient tensor F: Generates a deformation gradient tensor field covering the entire computational domain.

[0082] Based on the deformation gradient tensor field obtained in the previous step, the structural stress field is calculated. According to the constitutive relation of concrete, the deformation gradient is converted into stress. For the elastic region (strain is less than the concrete cracking strain), the generalized Hooke's law is used to calculate the stress: σ = D × ε, where σ is the stress tensor, D is the elastic stiffness matrix, and ε is the strain tensor, which is calculated by the deformation gradient. The elastic stiffness matrix D is determined by the elastic modulus E and Poisson's ratio μ of the concrete. The specific value is extracted from the time-varying characteristic data obtained in step S12, and the influence of the concrete age on the elastic modulus E (t) needs to be considered. For areas where the strain exceeds the concrete cracking strain, the damage constitutive model is used to calculate the stress. For example, the diffuse crack model is used, the damage variable d is introduced, and the stress calculation formula is corrected to: σ = (1-d) × D × ε. The value range of the damage variable d is 0 to 1, 0 means no damage, and 1 means complete damage. The damage variable d is calculated according to the strain size. The calculated stress tensor σ (x, y, z) is displayed in the form of a cloud map to obtain the structural stress field.

[0083] Select key sections in the post-casting zone area. For example, select sections at the junction of the post-casting zone and the cast part, and at the midpoint of the post-casting zone. For each key section, calculate the internal force of the section by integrating the stress field. For plate structures, calculate the bending moments mx, my and torque mxy per unit width. Use the numerical integration method for calculation. For example, for the bending moment mx, the calculation formula is: mx = ∫σx × zdz, where σx is the normal stress in the x-direction, z is the distance from the integration point to the neutral axis, and the integration interval is the section height. Use the Gaussian integration method for numerical integration. Divide the integration interval into several sub-intervals, select Gaussian integration points in each sub-interval, calculate the stress values ​​at the integration points, and then perform weighted summation. Similarly, calculate the bending moment my and torque mxy. Arrange the calculated key section internal force data (mx, my, mxy) in tabular form.

[0084] Compare the key section internal forces calculated in the previous step with the theoretical internal forces under the structural design load. Obtain the design load from the design drawings, including dead loads and live loads. According to the principles of structural mechanics, calculate the theoretical internal force distribution under the design load. Calculate the mechanical imbalance. For each key section, calculate the imbalance of its bending moment mx, my and torque mxy: ζmx = (mx, calculated-mx, design) / mx, design, ζmy = (my, calculated-my, design) / my, design, ζmxy = (mxy, calculated-mxy, design) / mxy, design, where mx, calculated, my, calculated, mxy, calculated are the calculated internal force values, and mx, design, my, design, mxy, design are the theoretical internal force values ​​under the design load. Set an imbalance threshold, such as ±15%. Mark areas where the imbalance exceeds the threshold as areas that need to be focused on. Generate a mechanical imbalance distribution map, using different colors to represent different degrees of imbalance.

[0085] Considering the mechanical imbalance and deformation amplitude comprehensively, the critical area is identified. Define the critical index CI = α × |ζ| + β × |δ|, where α and β are weight coefficients, representing the importance of mechanical imbalance and relative deformation, respectively, which can be determined based on engineering experience, such as α = 0.6, β = 0.4; ζ is the imbalance, taking the maximum value among ζmx, ζmy and ζmxy; δ is the relative deformation value, taking the maximum relative displacement relative to the adjacent cast area in the calibrated deformation field. Calculate the CI value of each critical area. Sort the critical areas according to the size of the CI value. Define the area where the CI value exceeds the set threshold as the critical control area. On the structural plan, mark the critical control area with different colors to generate a critical control area map.

[0086] Based on the principle of mechanical equilibrium, calculate the support force required to maintain the stability of the critical area. For each critical area, calculate the required support force according to its imbalance and internal force value. For example, for areas with unbalanced bending moments, the calculation formula for the required support force P is: P = ζm × M / L, where ζm is the imbalance of the bending moment, M is the design bending moment, and L is the distance from the support point to the line of action of the bending moment. Consider the actual layout possibility of the support system and adjust the support force. For example, decompose the support force into horizontal and vertical components, and determine the direction and point of action of the support force according to the type of support structure (for example, steel pipe support, adjustable steel support, etc.). Generate support force distribution data, including the position of the support point, the direction and magnitude of the support force.

[0087] Apply the support force distribution data calculated in the previous step as an external load to the finite element model. Re-analyze the structure and calculate the internal force distribution after the support is applied. Compare the internal force after the support is applied with the internal force before the support is applied. Calculate the internal force change rate: η = (Fafter-Fbefore) / Fbefore, where Fafter is the internal force after the support is applied, and Fbefore is the internal force before the support is applied. Check whether the internal force change rate is within a reasonable range, for example, whether the η value of all key areas is less than 10%. If the internal force change rate is too large, it means that the support force is too large or the layout is unreasonable and needs to be adjusted. Output the final internal force distribution data (including axial force, bending moment, shear force) in the form of a cloud map or table.

[0088] Preferably, the physical parameter-support force response analysis in step S3 includes:

[0089] Determine the sensitive interval of physical parameters according to the physical property spectrum of concrete materials and obtain a parameter sensitive interval table;

[0090] According to the parameter sensitive range table and the support point arrangement plan, the support force response test design is carried out to obtain the response test plan;

[0091] Collect physical parameter-support force response data according to the response test plan;

[0092] Analyze the single parameter response curve of physical parameter-support force response data;

[0093] Generate a parameter interaction matrix based on physical parameter-support force response data and single parameter response curves;

[0094] According to the physical parameter-support force response data and the physical property spectrum of concrete materials, the time-varying response characteristics are analyzed to obtain the time-varying response function;

[0095] The physical parameter-support force response function is constructed based on the time-varying response function, parameter interaction matrix and single parameter response curve.

[0096] In an embodiment of the present invention, a concrete material physical property spectrum (CMPS) is obtained from step S26. CMPS contains multi-dimensional information such as time, strain rate, temperature, and humidity. The influence of each physical parameter in CMPS on the strain rate is analyzed. For each physical parameter (temperature, humidity, age, etc.), determine the range of values ​​that significantly affects the structural deformation. For example, by analyzing the temperature-deformation rate relationship curve, it is found that when the temperature is in the range of 20°C-30°C, the effect of temperature change on the strain rate is most significant, and 20°C-30°C is defined as the sensitive interval of temperature. Similarly, the humidity-shrinkage rate relationship curve is analyzed to determine the sensitive interval of humidity. Analyze the time-strain rate relationship curve to determine the sensitive interval of age. The sensitive intervals of each physical parameter are organized into a table to form a parameter sensitive interval table. The table contains the name, symbol, unit and sensitive interval range of each physical parameter.

[0097] According to the parameter sensitivity interval table obtained in the previous step, determine the physical parameters and their range of variation that need to be controlled in the test. According to the support point arrangement scheme determined in step S3, design a scaled model test that simulates the actual working conditions. For example, a scaled model of the concrete slab and the support system is made at a ratio of 1:2. Pre-embed sensors of the same model and arrangement as the actual project in the model. Design the test loading scheme. According to the load conditions of the actual project, determine the loading method and loading level of the model test. For example, a graded loading method is used to gradually increase the load to simulate the force changes during concrete pouring and curing. Design the test control system. A hydraulic servo control system is used to accurately control the support force of the support system. For example, the MTS hydraulic servo loading system is used to simulate the stiffness changes of the support system through the displacement control mode. Develop detailed test steps and data acquisition plans. Clarify the duration, loading rate, data acquisition frequency, etc. of each test stage. Organize the test plan into a document to form a response test plan.

[0098] Carry out model test according to the response test plan formulated in the previous step. During the test, strictly control the environmental conditions to keep the temperature and humidity within the range specified in the parameter sensitivity range table. Use the MTS hydraulic servo loading system to load the model according to the loading method and loading level specified in the test plan. At the same time, adjust the support force of the support system through the hydraulic servo control system. Use sensors embedded in the model to collect real-time data on the strain, temperature, humidity of the concrete and the support force of the support system. The data acquisition frequency is set to 1Hz. Store the collected data in a CSV format file with the file name containing the test date and test number. The data file contains the following fields: timestamp, sensor number, strain value, temperature value, humidity value, and support force value.

[0099] Process the physical parameter-support force response data collected in the previous step. Draw a single parameter response curve. For each physical parameter (temperature, humidity, age, support force, etc.), draw a relationship curve between it and the strain of the key point of the structure. For example, draw a temperature-strain curve with the horizontal axis being the temperature and the vertical axis being the strain of the key point. Similarly, draw a humidity-strain curve, an age-strain curve, a support force-strain curve, etc. Analyze the characteristics of the curve. Observe the slope, curvature, inflection point and other characteristics of the curve to determine the degree and law of influence of each physical parameter on the strain. For example, if the slope of the temperature-strain curve is large, it means that the influence of temperature on the strain is more significant. If there is an inflection point on the curve, it means that the influence of the physical parameters on the strain has changed near this point.

[0100] Based on the single parameter response curve obtained in the previous step, analyze the interaction between different physical parameters. Design orthogonal experiments to study the effects of different physical parameter combinations on structural strain. For example, select three parameters: temperature, humidity, and support force, select three levels for each parameter, and conduct L9 (3^4) orthogonal experiments. According to the results of the orthogonal experiment, calculate the strain of the key points of the structure under each parameter combination. Calculate the parameter interaction coefficient. For every two parameters, calculate their interaction coefficient: Iij = (εij-εi-εj+ε0) / (εi×εj), where εij is the strain under the joint action of parameter i and parameter j, εi is the strain under the action of parameter i alone, εj is the strain under the action of parameter j alone, and ε0 is the baseline strain (the strain when all parameters are at the baseline value). Arrange the interaction coefficients in a matrix form to form a parameter interaction matrix. This matrix reflects the degree of mutual influence between different physical parameters.

[0101] Combine the model test data and CMPS to analyze the time-varying characteristics of the support force response. Select long-term test data to observe the change of the support force over time. Consider the influence of creep and shrinkage of concrete on the support force. Extract the creep coefficient and shrinkage strain change data over time from CMPS. Establish a time-varying response model of the support force. For example, use the exponential function form: F(t) = F0×(1+a×e^(-b×t)), where F(t) is the support force at time t, F0 is the initial support force, and a and b are time-varying coefficients determined by fitting the test data. Express the fitted time-varying response function in the form of a formula.

[0102] Taking into account the single parameter response, parameter interaction and time-varying characteristics, the physical parameter-support force response function (PSRF) is constructed. PSRF is a multivariable function whose input is various physical parameters (temperature, humidity, age, support force, etc.) and whose output is the strain of key points of the structure. The functional form of PSRF can be expressed as: ε = f (T, H, t, F, I, φ), where T is temperature, H is humidity, t is age, F is support force, I is the parameter interaction matrix, and φ is the time-varying response function. The specific expression of PSRF is obtained through multivariate regression analysis. The least squares method is used to fit the various coefficients in PSRF with the model test data as the target value. The final PSRF is expressed in the form of a formula, which is the mapping relationship between the physical properties of concrete and the optimal support force.

[0103] Preferably, the deformation characteristic anisotropy analysis in step S4 includes:

[0104] The measured deformation data is monitored and spatial distribution is sorted to obtain spatial deformation data;

[0105] Calculate the directional strain field of spatial deformation data;

[0106] Construct the deformation characteristic tensor field according to the directional strain field and the physical property spectrum of concrete material;

[0107] Perform deformation principal axis analysis on the deformation characteristic tensor field to obtain principal axis direction data;

[0108] Calculate the anisotropic intensity distribution based on the principal axis direction data;

[0109] Anisotropic deformation classification is performed based on anisotropic intensity distribution and principal axis direction data to obtain anisotropic type maps;

[0110] Determine the deformation main control direction diagram according to the anisotropy type diagram and the principal axis direction data;

[0111] Deformation anisotropy features are generated based on deformation dominant directions, anisotropy intensity distribution, and anisotropy type maps.

[0112] In an embodiment of the present invention, measured deformation data, i.e., corrected strain data, is obtained from step S25. These data are stored in the form of a time series, including the number, position, and strain value of each sensing node at different times. The strain data is converted into displacement data. For each sensing node, its displacement components in the three directions of x, y, and z are calculated according to its position and strain value. For example, for a strain sensor arranged along the x direction, the displacement calculation formula is: ux=εx×L, where εx is the corrected x-direction strain, and L is the effective length of the sensor. For areas where the displacement cannot be measured directly, a spatial interpolation method is used for estimation. For example, the Kriging interpolation method is used to estimate the displacement value of an unknown point based on the displacement data of a known point. The displacement data of all nodes are sorted according to their spatial coordinates (x, y, z) to form a spatial deformation data set. The data set contains the coordinates of each point and the displacement vectors (ux, uy, uz) at different times.

[0113] Based on the spatial deformation data set obtained in the previous step, the directional strain of each point is calculated. The strain components are calculated using the finite difference method. For each point, the normal strain in the x, y, and z directions is calculated: For example, for point i, the normal strain in the x direction is calculated as: εx,i = (ux,i+1-ux,i-1) / (xi+1-xi-1), where ux,i+1 and ux,i-1 are the x-direction displacement components of the adjacent points of point i in the x direction, and xi+1 and xi-1 are the x-coordinates of the adjacent points. Similarly, the shear strain is calculated as: The calculated strain components are combined into a strain tensor ε: ε = [[εx, γxy, γxz], [γyx, εy, γyz], [γzx, γzy, εz]]. A directional strain field covering the entire analysis area is generated, which contains the strain information of each point in different directions.

[0114] Combine the directional strain field obtained in the previous step with the concrete material physical property spectrum (CMPS) obtained in step S26. CMPS contains the mechanical performance parameters of concrete materials under different time, temperature and humidity conditions. According to the strain state of each point and the corresponding material parameters in CMPS, a deformation characteristic tensor is constructed. The deformation characteristic tensor D is a second-order tensor used to describe the deformation ability of the material in different directions. For isotropic materials, D can be simplified to a diagonal matrix, and the diagonal elements are Young's modulus E. For anisotropic materials, D is a full-rank matrix whose elements are determined by the elastic modulus and Poisson's ratio of the material in different directions. Considering the influence of factors such as the age and creep of concrete on the deformation characteristics, the elements in D are corrected. For example, according to the change law of the creep coefficient in CMPS over time, the elastic modulus value in D is adjusted. Generate a deformation characteristic tensor field covering the entire analysis area, which describes the deformation ability of the material in different positions and directions.

[0115] Perform eigenvalue decomposition on the deformation characteristic tensor field obtained in the previous step. For each point of the deformation characteristic tensor D, solve its eigenvalues ​​and eigenvectors. The eigenvalues ​​λ1, λ2, and λ3 represent the deformation capacity of the material in the three main directions, and the eigenvectors v1, v2, and v3 represent the directions of the three main directions. Arrange the eigenvalues ​​in order from large to small: λ1≥λ2≥λ3. The corresponding eigenvectors v1, v2, and v3 are the directions of the deformation principal axes. Calculate the angle between the eigenvector and the global coordinate axis (x, y, z). For example, calculate the angle between v1 and the x-axis: θx = arccos(v1·x / |v1||x|), where v1·x represents the dot product of vectors v1 and x, and |v1| and |x| represent the modulus of the vector. Similarly, calculate the angle between v1 and the y-axis and z-axis, as well as the angle between v2 and v3 and the coordinate axis. The main axis direction data (eigenvector or angle with the coordinate axis) of each point is stored to form the main axis direction data.

[0116] Based on the deformation principal axis analysis results obtained in the previous step, calculate the anisotropic strength. Anisotropic strength is used to quantify the degree of difference in the deformation ability of the material in different directions. Calculate the principal strain ratios: κ1 = λ1 / λ2, κ2 = λ1 / λ3, κ3 = λ2 / λ3, where λ1, λ2, and λ3 are three principal strains (eigenvalues). The larger the κ value, the stronger the deformation ability of the material in this direction relative to other directions. According to the size of the κ value, the degree of anisotropy is judged. For example, if κ1 is much larger than 1, while κ2 and κ3 are close to 1, it means that the material has a significant deformation advantage in the first principal direction, showing unidirectional anisotropy. If κ1, κ2, and κ3 are all close to 1, it means that the material is close to isotropy. Generate anisotropic strength distribution diagram, using different colors to represent different sizes of κ values.

[0117] Based on the anisotropic strength distribution and principal axis direction data obtained in the previous step, classify the deformation characteristics. Set the classification criteria. For example: Type I (weak anisotropy): all κ values ​​are less than 1.2. Type II (unidirectional dominance): κ1 is greater than 1.5, and κ2 and κ3 are less than 1.2. Type III (planar dominance): κ1 and κ2 are both greater than 1.5, and κ3 is less than 1.2. Type IV (complete anisotropy): all κ values ​​are greater than 1.5 and are significantly different from each other. Classify each point to determine which type it belongs to. Generate an anisotropy type map, using different colors to represent different types of areas.

[0118] According to the anisotropy type diagram and principal axis direction data obtained in the previous step, determine the main control direction of deformation in each region. For type II (unidirectional advantage) regions, the main control direction is the direction of the eigenvector corresponding to the maximum eigenvalue. For type III (plane advantage) regions, the main control direction is the plane spanned by the eigenvectors corresponding to the two larger eigenvalues. For type I (weak anisotropy) and type IV (complete anisotropy) regions, determine the main control direction based on actual engineering needs. For example, the eigenvector direction corresponding to the maximum eigenvalue can be selected as the main control direction. Generate a deformation main control direction diagram, and use arrows to indicate the main control direction of each region.

[0119] The deformation main control direction map, anisotropy strength distribution map and anisotropy type map obtained in the previous step are combined to form a complete deformation anisotropy feature description, including: three-dimensional distribution map of the main axis direction: showing the direction and distribution of the deformation main axis in space. Anisotropy strength cloud map: intuitively displays the spatial distribution of the κ value, reflecting the difference in the degree of anisotropy in different regions. Deformation type area division: mark the regional range of different anisotropy types. Main control direction vector field: use arrows to indicate the main control direction of each area, indicating the main trend of deformation. These comprehensive features provide multi-dimensional information for support layout optimization, ensuring that the support system can control anisotropic deformation in a targeted manner.

[0120] Preferably, the structural mechanics transfer path analysis in step S4 includes:

[0121] Gridding the internal force distribution data to obtain gridded internal force distribution data;

[0122] Calculate the force flow vector field based on the gridded internal force distribution data;

[0123] Extract the main flow path data of the force flow vector field;

[0124] Identify key nodes of the main flow path data;

[0125] According to the gridded internal force distribution data, the node mechanical coupling analysis is performed on the key nodes to obtain the node coupling relationship matrix;

[0126] Construct the main force transmission link according to the node coupling relationship matrix and the main flow path data;

[0127] The support control area is divided according to the main force transmission link and key nodes to obtain a support control area map;

[0128] A mechanical transfer path diagram is generated based on the support control area diagram, the main force transmission link and the key nodes.

[0129] In an embodiment of the present invention, internal force distribution data is obtained from step S3, and the data includes the internal force distribution of the structure after the support force is applied, including axial force, bending moment, shear force, etc. Since the internal force distribution data is usually given based on the nodes or units of the finite element model, it is necessary to convert it into regular grid data. An interpolation method is used to map the irregularly distributed internal force data to regular grid points. For example, the inverse distance weighted interpolation method is used to calculate the internal force values ​​at the grid points based on the internal force values ​​of the known points. The density of the grid is determined according to actual needs. For example, the same density as the finite element grid can be used, or a denser grid can be used. The internal force data obtained by interpolation is stored as gridded data, and each grid point contains the coordinates of the point and the corresponding internal force value (axial force, bending moment, shear force, etc.).

[0130] Based on the gridded internal force distribution data obtained in the previous step, calculate the force flow vector field. The force flow vector represents the direction and magnitude of the internal force in the structure. For planar problems, the force flow vector can be calculated by the gradient of the internal force components (for example, the bending moment mx, my and torque mxy per unit width). Calculate the internal force gradient. For each grid point, calculate its internal force gradient in the x and y directions: The central difference method is used to calculate the numerical gradient. For example, for grid point i, the bending moment gradient in the x direction is calculated as: Where mx,i+1 and mx,i-1 are the x-direction bending moments of the adjacent grid points of grid point i in the x direction, and xi+1 and xi-1 are the x-coordinates of the adjacent grid points. Construct the force flow vector. For each grid point, its force flow vector v can be expressed as: The direction of the force flow vector indicates the main direction of internal force transmission, and the magnitude of the force flow vector indicates the intensity of internal force transmission. The force flow vectors of all grid points are combined together to form a force flow vector field.

[0131] Based on the force flow vector field obtained in the previous step, the main flow path is extracted. The main flow path represents the main path for internal force transmission, and is usually distributed along the area where the force flow vector is densest and the force flow intensity is the largest. The main flow path is extracted using the streamline method. Starting from the starting point, gradually trace along the direction of the force flow vector to form a streamline. The choice of the starting point can be determined based on engineering experience. For example, the load point or the point with the maximum internal force can be selected as the starting point. The step size of the streamline can be determined according to actual needs. For example, the grid spacing can be used as the step size. When the streamline encounters a boundary or a point where the force flow vector is zero, stop tracing. Repeat the above process to generate multiple streamlines that cover the entire force flow vector field. The extracted streamline data is stored as a series of point coordinates to form the main flow path data.

[0132] Based on the main flow path data obtained in the previous step, identify key nodes. Key nodes are important nodes on the main flow path, usually points where internal forces are concentrated, force flow direction changes significantly, or multiple main flow paths intersect. Analyze the curvature of the main flow path. Calculate the curvature of each point on each main flow path. The larger the curvature, the more drastic the change in force flow direction. Define the points where the curvature exceeds the set threshold as key nodes. Analyze the intersection of the main flow path. Define the points where multiple main flow paths intersect as key nodes. Analyze the internal force gradient. Calculate the internal force gradient on each main flow path, and define the points where the internal force gradient exceeds the set threshold as key nodes. Mark the identified key nodes and record their coordinates and properties (for example, curvature, internal force gradient, etc.).

[0133] Based on the key nodes identified in the previous step and the gridded internal force distribution data, analyze the mechanical coupling relationship between the nodes. For each key node, examine the internal force distribution in the surrounding area. Select grid points within a certain range centered on the key node, for example, select a 3×3 or 5×5 grid point area. Calculate the internal force transfer relationship between the key node and the grid points around it. For example, calculate the correlation coefficient between the bending moment at the key node and the bending moment at the grid points around it. Construct a node coupling relationship matrix. The rows and columns of the matrix represent the key nodes, respectively, and the matrix elements represent the coupling strength between the nodes. The coupling strength can be represented by indicators such as correlation coefficient and mutual information. For example, if the correlation coefficient between two nodes is close to 1, it means that the coupling strength between them is very high and the internal force transmission is close; if the correlation coefficient is close to 0, it means that the coupling strength between them is very low and the internal force transmission is weak. Store the node coupling relationship matrix.

[0134] Based on the node coupling relationship matrix and main flow path data obtained in the previous step, the main force transmission link is constructed. The main force transmission link is a path that connects key nodes and reflects the main force path of the structure. According to the node coupling relationship matrix, the connection strength between key nodes is determined. For example, the node pairs with coupling strength greater than the set threshold are defined as strong connections. According to the main flow path data, the connection direction between key nodes is determined. For example, the key nodes are connected along the direction of the main flow path. The strongly connected node pairs are connected according to the connection direction to form the main force transmission link. There are multiple main force transmission links, each representing a different force path.

[0135] Based on the main force transmission link and key nodes obtained in the previous step, the support control area is divided. The support control area is the area that needs to be focused on and controlled, usually located near the main force transmission link or around the key nodes. With the main force transmission link as the skeleton, a certain range is extended to both sides to form a support control area. The extension range can be determined based on engineering experience. For example, 1-2 times the plate thickness can be taken as the extension range. The area where the key node is located is also included in the support control area. The support control area is marked with different colors to form a support control area map.

[0136] The support control area diagram, main force transmission link and key nodes obtained in the previous step are combined to generate a mechanical transfer path diagram. The mechanical transfer path diagram is a diagram that reflects the overall force characteristics of the structure and contains the following information: Main force transmission link: represented by a thick solid line, indicating the main force path of the structure. Key nodes: marked with special symbols (for example, circles, squares, etc.), indicating points where internal forces are concentrated or the direction of force flow changes significantly. Support control area: filled with different colors to indicate areas that require special attention and control. The mechanical transfer path diagram provides an intuitive basis for the optimization of support layout, which helps to determine the optimal layout position of the support system and the control strategy of the support force.

[0137] Preferably, the support layout optimization and control efficiency evaluation in step S4 includes:

[0138] According to the mechanical transfer path diagram and the support point arrangement plan, the mechanical contribution of the support points is evaluated to obtain the mechanical contribution matrix;

[0139] Generate an optimized support layout plan based on the mechanical contribution matrix;

[0140] The structural deformation control efficiency of the optimized support layout scheme is evaluated to obtain the structural deformation control efficiency index.

[0141] In an embodiment of the present invention, a mechanical transmission path diagram is obtained from step S4, which includes information such as the main force transmission link, key nodes and support control area. A preliminary support point arrangement scheme is obtained from step S3, which determines the initial position of the support point. A finite element model is established, which includes a structure and a support system. The support system is parameterized and the position, direction and stiffness of each support point can be adjusted independently. The mechanical contribution of the support points is evaluated one by one. For each support point i: 1. In the finite element model, the support point is temporarily removed. 2. Structural analysis is performed to calculate the deformation and internal force of the structure. 3. The structural response (e.g., maximum displacement, maximum stress, etc.) after removing the support point i is compared with the structural response when the support point is not removed. 4. The mechanical contribution Ci of the support point i is calculated: Ci = (R0-Ri) / R0, where R0 is the structural response when no support point is removed, and Ri is the structural response after the support point i is removed. The mechanical contributions of all support points are arranged in a matrix form to form a mechanical contribution matrix. The rows and columns of the matrix represent the support points, and the matrix element Ci represents the mechanical contribution of the support point i.

[0142] Based on the mechanical contribution matrix obtained in the previous step, the support point layout is optimized. A greedy algorithm is used for optimization.

[0143] 1. Find the support point with the greatest contribution from the mechanical contribution matrix and add it to the optimized support layout plan.

[0144] 2. From the remaining support points, find the support point with the highest contribution again. When selecting, consider the interaction between the newly added support point and the selected support point. For example, if two support points are very close, their effects will overlap, resulting in a lower contribution.

[0145] 3. Repeat step 2 until the preset stop condition is met. The stop condition can be:

[0146] The number of support points reaches the preset upper limit.

[0147] The deformation or internal force of the structure meets the design requirements.

[0148] Continuing to add support points does not significantly improve the structural response.

[0149] The final selected support points and their positions, directions and stiffness parameters are documented to form an optimized support layout plan.

[0150] Based on the optimized support layout scheme obtained in the previous step, evaluate its control effect on structural deformation. Establish the Structural Deformation Control Effectiveness Index (SCEI). SCEI comprehensively considers the following three key indicators:

[0151] 1. Support point mechanical contribution (M): It indicates the control ability of the support system on structural deformation. The larger the M value, the stronger the control ability. M can be the average or weighted average of the mechanical contributions of all support points.

[0152] 2. Material efficiency (E): It indicates the utilization rate of the materials used in the support system. The larger the E value, the higher the material utilization rate. E can be defined as: E = total stiffness provided by the support system / total weight of the support system.

[0153] 3. Space utilization (S): Indicates the degree of influence of the support system on the construction space. The larger the S value, the smaller the influence on the construction space. S can be defined as: S = space not occupied by the support system / total construction space.

[0154] Construct a calculation formula for SCEI. For example, the weighted average method can be used: SCEI = w1×M+w2×E+w3×S, where w1, w2, and w3 are weight coefficients, which respectively represent the importance of the three indicators, and their value range is 0 to 1, and w1+w2+w3=1. The value of the weight coefficient can be determined based on engineering experience or expert opinion. Calculate the SCEI value of the optimized support layout scheme. Substitute the various parameters of the optimized support layout scheme (support point location, stiffness, quantity, etc.) into the SCEI calculation formula to obtain the SCEI value. Compare the SCEI value with the SCEI values ​​of other alternative schemes, and select the scheme with the highest SCEI value as the final support layout scheme.

[0155] Preferably, the present invention further provides a concrete floor post-casting belt intelligent telescopic inductive support construction device, comprising a support device main body, a sensor network part, a data processing control part and a power supply part, wherein the sensor network part is installed on the support device main body, the power supply part is installed inside the support device main body, and the data processing control part is electrically connected to the power supply part, and is used to execute the above-mentioned concrete floor post-casting belt intelligent telescopic inductive support construction method, wherein the data processing control part of the concrete floor post-casting belt intelligent telescopic inductive support construction device comprises:

[0156] The structural deformation characteristic modeling module is used to analyze the time-varying characteristics of concrete foundation parameters to obtain time-varying characteristic data; calculate the temperature-contraction coupling effect parameters based on the time-varying characteristic data to obtain the temperature-contraction coupling parameters; and construct the structural deformation mechanical response tensor based on the time-varying characteristic data;

[0157] Intelligent monitoring module, used to identify deformation sensitive areas according to the mechanical response tensor of structural deformation; collect sensor network data and analyze physical quantity mapping relationship according to temperature-contraction coupling parameters; construct physical property spectrum of concrete material according to physical quantity mapping relationship;

[0158] The support force intelligent control module is used to obtain real-time monitoring data; perform structural force analysis based on the real-time monitoring data to obtain internal force distribution data; determine the support point layout plan based on the internal force distribution data and deformation sensitive areas; perform physical parameter-support force response analysis based on the physical property spectrum of concrete materials and the support point layout plan to obtain the physical parameter-support force response function; and use the physical parameter-support force response function to control the support force in real time;

[0159] The support layout optimization module is used to obtain the measured deformation data; perform anisotropy analysis of deformation characteristics based on the measured deformation data to obtain the anisotropic characteristics of deformation; perform structural mechanics transfer path analysis based on the anisotropic characteristics of deformation to obtain a mechanics transfer path diagram; perform support layout optimization and control efficiency evaluation on the support point arrangement scheme based on the mechanics transfer path diagram to obtain the structural deformation control efficiency index.

[0160] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0161] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A construction method for intelligent telescopic induction support of post-cast belts of concrete floor, characterized in that: The following steps are involved: Step S1: analyzing the time-varying characteristics of concrete foundation parameters to obtain time-varying characteristic data; calculating the temperature-contraction coupling effect parameters according to the time-varying characteristic data to obtain the temperature-contraction coupling parameters; and constructing the structural deformation mechanical response tensor according to the time-varying characteristic data; Step S2: identifying deformation sensitive areas according to the structural deformation mechanical response tensor; Collect sensor network data and analyze physical quantity mapping relationships based on temperature-contraction coupling parameters; construct physical property spectrum of concrete materials based on physical quantity mapping relationships; Step S3: acquiring real-time monitoring data; performing structural force analysis based on the real-time monitoring data to obtain internal force distribution data; determining a support point arrangement scheme based on the internal force distribution data and the deformation sensitive area; performing a physical parameter-support force response analysis based on the physical property spectrum of the concrete material and the support point arrangement scheme to obtain a physical parameter-support force response function; and using the physical parameter-support force response function to regulate the support force in real time; Step S4: obtaining measured deformation data; Anisotropic analysis of deformation characteristics is performed based on measured deformation data to obtain deformation anisotropic characteristics; The structural mechanical transfer path analysis is carried out according to the deformation anisotropy characteristics to obtain the mechanical transfer path diagram; the support point arrangement scheme is optimized and the control efficiency is evaluated according to the mechanical transfer path diagram to obtain the structural deformation control efficiency index.

2. The intelligent telescopic induction support construction method for post-cast strips of concrete slabs according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: collecting concrete foundation parameters and performing characteristic measurement to obtain concrete foundation characteristic data; Step S12: performing time-varying characteristic evolution processing according to the concrete foundation characteristic data to obtain time-varying characteristic data; Step S13: Calculating the temperature-contraction coupling effect parameters according to the time-varying characteristic data to obtain the temperature-contraction coupling parameters; Step S14: Acquire engineering structure data, and construct a structural deformation mechanical response tensor based on the time-varying characteristic data.

3. The intelligent telescopic induction support construction method for post-cast strips of concrete slabs according to claim 2 is characterized in that: Step S13 includes the following steps: Step S131: determining the shrinkage sensitivity coefficient of concrete; Step S132: constructing a temperature influence correction curve according to the shrinkage sensitivity coefficient to obtain a temperature correction function; Step S133: constructing a humidity influence correction curve according to the shrinkage sensitivity coefficient to obtain a humidity correction function; Step S134: performing temperature-humidity interaction analysis according to the temperature correction function and the humidity correction function to obtain an interaction influence matrix; Step S135: constructing a temperature-contraction coupling model according to the interaction influence matrix, the temperature correction function and the humidity correction function to obtain a coupled deformation prediction model; Step S136: Calculating temperature-contraction coupling effect parameters using the coupled deformation prediction model; Step S137: performing coupling effect time-varying characteristic analysis according to the temperature-contraction coupling effect parameters and the time-varying characteristic data to obtain the temperature-contraction coupling parameters.

4. The intelligent telescopic induction support construction method for post-cast strips of concrete floor according to claim 1 is characterized in that: Step S2 includes the following steps: Step S21: identifying a deformation sensitive region according to the structural deformation mechanical response tensor, wherein the deformation sensitive region is a region where the strain gradient modulus is greater than 1.5 times the average value; Step S22: deploying a multi-parameter sensor array in the deformation sensitive area, wherein the sensor network in the multi-parameter sensor array is arranged every 1 m along the length direction of the board and every 0.5 m along the width direction of the board in the deformation sensitive area; Step S23: collecting sensor network data using a multi-parameter sensor array; Step S24: analyzing the strain-temperature-humidity three-dimensional mapping relationship according to the sensor network data and the temperature-contraction coupling parameter to obtain a physical quantity mapping relationship; Step S25: generating a strain correction value according to the physical quantity mapping relationship and the sensor network data; Step S26: constructing a physical property spectrum of concrete material according to the strain correction value and the sensor network data.

5. The intelligent telescopic induction support construction method for post-cast strips of concrete floor according to claim 1 is characterized in that: The structural stress analysis in step S3 includes: Perform monitoring data calibration and integration on the real-time monitoring data and the mechanical response tensor of the structural deformation to obtain a calibrated deformation field; generating a deformation gradient field according to the calibration deformation field; The stress field is derived from the deformation gradient field to obtain the structural stress field; Calculate the cross-sectional internal force of the structural stress field and obtain the key cross-sectional internal force data; The mechanical imbalance analysis is performed on the internal force data of the key sections to obtain the mechanical imbalance distribution diagram, where the threshold of the mechanical imbalance is set at ±15%; According to the mechanical imbalance distribution map and the calibrated deformation field, the critical area is identified to obtain the critical control area map; Calculate the required support force based on the critical control area diagram and the key section internal force data to obtain the support force distribution data; Generate internal force distribution data based on support force distribution data and critical control area diagram.

6. The intelligent telescopic induction support construction method for post-cast strips of concrete floor according to claim 1 is characterized in that: The physical parameter-support force response analysis in step S3 includes: Determine the sensitive interval of physical parameters according to the physical property spectrum of concrete materials and obtain a parameter sensitive interval table; According to the parameter sensitive range table and the support point arrangement plan, the support force response test design is carried out to obtain the response test plan; Collect physical parameter-support force response data according to the response test plan; Analyze the single parameter response curve of physical parameter-support force response data; Generate a parameter interaction matrix based on physical parameter-support force response data and single parameter response curves; According to the physical parameter-support force response data and the physical property spectrum of concrete materials, the time-varying response characteristics are analyzed to obtain the time-varying response function; The physical parameter-support force response function is constructed based on the time-varying response function, parameter interaction matrix and single parameter response curve.

7. The intelligent telescopic induction support construction method for post-cast strips of concrete floor according to claim 1 is characterized in that: The anisotropic analysis of deformation characteristics in step S4 includes: The measured deformation data is monitored and spatial distribution is sorted to obtain spatial deformation data; Calculate the directional strain field of spatial deformation data; Construct the deformation characteristic tensor field according to the directional strain field and the physical property spectrum of concrete material; Perform deformation principal axis analysis on the deformation characteristic tensor field to obtain principal axis direction data; Calculate the anisotropic intensity distribution based on the principal axis direction data; Anisotropic deformation classification is performed based on anisotropic intensity distribution and principal axis direction data to obtain anisotropic type maps; Determine the deformation main control direction diagram according to the anisotropy type diagram and the principal axis direction data; Deformation anisotropy features are generated based on deformation dominant directions, anisotropy intensity distribution, and anisotropy type maps.

8. The intelligent telescopic induction support construction method for post-cast strips of concrete slabs according to claim 1 is characterized in that: The structural mechanics transfer path analysis in step S4 includes: Gridding the internal force distribution data to obtain gridded internal force distribution data; Calculate the force flow vector field based on the gridded internal force distribution data; Extract the main flow path data of the force flow vector field; Identify key nodes of the main flow path data; According to the gridded internal force distribution data, the node mechanical coupling analysis is performed on the key nodes to obtain the node coupling relationship matrix; Construct the main force transmission link according to the node coupling relationship matrix and the main flow path data; The support control area is divided according to the main force transmission link and key nodes to obtain a support control area map; A mechanical transfer path diagram is generated based on the support control area diagram, the main force transmission link and the key nodes.

9. The intelligent telescopic induction support construction method for post-cast strips of concrete slabs according to claim 1 is characterized in that: The support layout optimization and control efficiency evaluation in step S4 includes: According to the mechanical transfer path diagram and the support point arrangement plan, the mechanical contribution of the support points is evaluated to obtain the mechanical contribution matrix; Generate an optimized support layout plan based on the mechanical contribution matrix; The structural deformation control efficiency of the optimized support layout scheme is evaluated to obtain the structural deformation control efficiency index.

10. An intelligent telescopic induction support construction device for a post-cast belt of a concrete floor, characterized in that: The invention comprises a supporting device body, a sensor network part, a data processing control part and a power supply part, wherein the sensor network part is installed on the supporting device body, the power supply part is installed inside the supporting device body, and the data processing control part is electrically connected to the power supply part, and is used to execute the intelligent telescopic inductive support construction method for post-casting strips of concrete floor slabs according to claim 1. The data processing control part of the intelligent telescopic inductive support construction device for post-casting strips of concrete floor slabs comprises: The structural deformation characteristic modeling module is used to analyze the time-varying characteristics of concrete foundation parameters to obtain time-varying characteristic data; calculate the temperature-contraction coupling effect parameters based on the time-varying characteristic data to obtain the temperature-contraction coupling parameters; and construct the structural deformation mechanical response tensor based on the time-varying characteristic data; Intelligent monitoring module, used to identify deformation sensitive areas according to the mechanical response tensor of structural deformation; collect sensor network data and analyze physical quantity mapping relationship according to temperature-contraction coupling parameters; construct physical property spectrum of concrete material according to physical quantity mapping relationship; The support force intelligent control module is used to obtain real-time monitoring data; perform structural force analysis based on the real-time monitoring data to obtain internal force distribution data; determine the support point layout plan based on the internal force distribution data and deformation sensitive areas; perform physical parameter-support force response analysis based on the physical property spectrum of concrete materials and the support point layout plan to obtain the physical parameter-support force response function; and use the physical parameter-support force response function to control the support force in real time; The support layout optimization module is used to obtain the measured deformation data; perform anisotropy analysis of deformation characteristics based on the measured deformation data to obtain the anisotropic characteristics of deformation; perform structural mechanics transfer path analysis based on the anisotropic characteristics of deformation to obtain a mechanics transfer path diagram; perform support layout optimization and control efficiency evaluation on the support point arrangement scheme based on the mechanics transfer path diagram to obtain the structural deformation control efficiency index.

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